# Detecting data-driven robust statistical arbitrage strategies with deep neural networks

arXiv: https://arxiv.org/abs/2203.03179 · HTML: https://mlsanity.com/paper/2203.03179/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Regularisation of Neural Networks by Enforcing Lipschitz Continuity](https://mlsanity.com/paper/1804.04368/) (2018)
- [Sorting out Lipschitz function approximation](https://mlsanity.com/paper/1811.05381/) (2018)
- [Universal Approximation with Deep Narrow Networks](https://mlsanity.com/paper/1905.08539/) (2019)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [On the regularization of Wasserstein GANs](https://mlsanity.com/paper/1709.08894/) (2017)
- [Forecasting directional movements of stock prices for intraday trading using LSTM and random forests](https://mlsanity.com/paper/2004.10178/) (2020)
- [(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual Algorithm](https://mlsanity.com/paper/1904.04546/) (2019)
- [Computation of optimal transport and related hedging problems via penalization and neural networks](https://mlsanity.com/paper/1802.08539/) (2018)
- [Robust risk aggregation with neural networks](https://mlsanity.com/paper/1811.00304/) (2018)
- [Low-Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization](https://mlsanity.com/paper/1908.00461/) (2019)
- [Robust pricing and hedging of options on multiple assets and its numerics](https://mlsanity.com/paper/1909.03870/) (2019)
- [Lipschitz neural networks are dense in the set of all Lipschitz functions](https://mlsanity.com/paper/2009.13881/) (2020)
- [A deep learning approach to data-driven model-free pricing and to martingale optimal transport](https://mlsanity.com/paper/2103.11435/) (2021)
- [Model-free bounds for multi-asset options using option-implied information and their exact computation](https://mlsanity.com/paper/2006.14288/) (2020)
- [Arbitrage and duality in nondominated discrete-time models](https://mlsanity.com/paper/1305.6008/) (2013)

## Built on by (most cited first, 4 of 4)

- [Chaotic Hedging with Iterated Integrals and Neural Networks](https://mlsanity.com/paper/2209.10166/) (2022)
- [Langevin dynamics based algorithm e-THvarepsilonO POULA for stochastic optimization problems with discontinuous stochastic gradient](https://mlsanity.com/paper/2210.13193/) (2022)
- [Markov Decision Processes under Model Uncertainty](https://mlsanity.com/paper/2206.06109/) (2022)
- [Neural networks can detect model-free static arbitrage strategies](https://mlsanity.com/paper/2306.16422/) (2023)

## Abstract (arXiv)

We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets. Robust statistical arbitrage strategies refer to trading strategies that enable profitable trading under model ambiguity. The presented novel methodology allows to consider a large amount of underlying securities simultaneously and does not depend on the identification of cointegrated pairs of assets, hence it is applicable on high-dimensional financial markets or in markets where classical pairs trading approaches fail. Moreover, we provide a method to build an ambiguity set of admissible probability measures that can be derived from observed market data. Thus, the approach can be considered as being model-free and entirely data-driven. We showcase the applicability of our method by providing empirical investigations with highly profitable trading performances even in 50 dimensions, during financial crises, and when the cointegration relationship between asset pairs stops to persist.